ern: An R package to estimate the effective reproduction number using clinical and wastewater surveillance data

The effective reproduction number, Rt, is an important epidemiological metric used to assess the state of an epidemic, as well as the effectiveness of public health interventions undertaken in response. When Rt is above one, it indicates that new infections are increasing, and thus the epidemic is growing, while an Rt is below one indicates that new infections are decreasing, and so the epidemic is under control. There are several established software packages that are readily available to statistically estimate Rt using clinical surveillance data. However, there are comparatively few accessible tools for estimating Rt from pathogen wastewater concentration, a surveillance data stream that cemented its utility during the COVID-19 pandemic. We present the R package ern that aims to perform the estimation of the effective reproduction number from real-world wastewater or aggregated clinical surveillance data in a user-friendly way.

The authors presented an R software package, which implements statistical methods to estimate the actual number of new infections using the number of reported cases or the wastewater data.It is important to note that the package allows the input data to be sampled by a period higher then one day (e.g., aggregated weekly data is also acceptable).Still, the output is a daily time series, which allows to estimate the effective reproduction number using the already existing tool, EpiEstim (Cori et al. 2013).To estimate the hidden time-series (i.e., the unknown input) from the measured output, the Authors applied a deconvolution using an existing Richardson-Lucy implementation.As far as I know, this technique is equivalent to a dynamic inversion (Silverman 1969 andIsidori 1999, Sec. 5.6), which was already used to infer the effective reproduction number R t (Csutak et al. 2023).
In the abstract, the authors very diplomatically note the lack of publicly available user-friendly statistical tools to easily estimate R t from wastewater data.I have looked through the software package myself, I have also tried it out, and I agree that it is user-friendly: • It requires a so-to-say lightweight and free programming environment, R (in contrast to a MATLAB package).
• It requires only a few 3rd party packages, like EpiEstim, assertthat, dplyr, tidyr, lubridate, patchwork, rjags, therefore, it can be easily installed.(This was the first time I used R.) • Example data are provided alongside the code, hence, the results from the manuscript can be easily reproduced.
• The code itself is nicely organized and well-parameterized.
However, I should note that I found a few -possibly not user-friendly, but publicly available -software tools to estimate the effective reproduction number (or at least the incidence) from wastewater data possibly in combination with the hospital load or reported cases.(I did not try them out myself): Not to mention the deconvolution-based method and the associated R code developed by Huisman et al. 2022 (referred to as "[14]"), which are publicly available at https://github.com/JSHuisman/wastewaterRe.The Authors also observed that their result "is similar to the one taken in [14]" (Lines 107-112).However, the Authors did not mention the real advantages of their software over [14] other than "user-friendliness".
• Function LOESS at Line 123 is not introduced, first, it seemed to be an abbreviation, then, after a short googling, I realized it is quite standard in statistics especially in R. I think it would be useful to add a reference here, e.g., the documentation page of LOESS (https://www.rdocumentation.org/packages/stats/versions/3.6.2/to • Line 251, "plot(dist.fec)"=⇒ plot dist(dist.fec)(?).
• I liked the idea that the parameters of the fecal shedding and symptom generation distributions were also considered as probability variables, and were sampled accordingly.
Overall impression and evaluation.
If I read the manuscript like a user's manual, it's nicely written.Although I cannot detect any scientific contribution in this manuscript, the "attached" R package may be useful for a certain community (e.g., public health practitioners).The manuscript has therefore a raison d'être, possibly not in such a high impact journal (but the Editor is the final judge on that).Anyway, the Authors need to be better justify why their software tool is preferable or more convenient compared to other existing R/Matlab/Python packages.

•
Proverbio et al. 2022have developed a MATLAB package, called the CoWWAn (COVID-19 Wastewater Analyser), which makes it possible to infer the shedding population and estimate the effective reproduction number.CoWWAn is available at https://gitlab.lcsb.uni.lu/SCG/cowwan.(See also Panel a Fazli et al. 20211proposed a technique to reconstruct the actual number of new cases from the clinical reported cases and/or wastewater data.The R code is available at https://github.com/Shakeri-Lab/COVID-SEIR.